Global Labor & Employment Data

Data

Available ActionsEach successful request consumes credits as outlined below.

query_labor_data^5cr

Details

Access workforce and employment data for any country in the world. Look up labor force participation rates, unemployment figures, sector breakdowns, gender employment gaps, and youth employment trends. Compare workforce metrics across countries, track labor market shifts over time, and benchmark regional performance — all from a comprehensive global data set.

Use Cases

Research labor force participation rates by country, Compare unemployment rates across regions, Analyze employment by sector breakdown, Study gender gaps in workforce participation, Track youth unemployment trends, Benchmark formal vs informal employment, Monitor wage and labor market shifts, Support workforce equity research, Assess progress toward SDG 8 decent work targets, Compare regional labor market performance

Connect Your Agent In 5 Min

Watch the setup guide for your platform

Or Install Locally

STDIO connector for Claude Code, Codex, Cursor, Zed, and other LLMs that require STDIO or custom connections. This lightweight connector routes requests to https://api.agentpmt.com/mcp. All tool execution happens in the cloud and the server cannot edit any files on your computer.

npm install -g @agentpmt/mcp-routeragentpmt-setup

Actions(1)

query_labor_data^5cr7 params(1 required)

Fetch labor market and employment indicator data for a country or region, including labor force participation, unemployment rates, sector employment, gender gap analysis, and employment quality metrics.

country_or_regionrequiredstring

Country or region name in plain language (e.g., 'Kenya', 'South Korea', 'World')

demographic_filterstring

Filter indicators by demographic: 'gender', 'youth', 'sector', 'total', or 'all' for all indicators

Values:

genderyouthsectortotalall

calculate_gender_gapsboolean

Calculate gender gaps (male rate minus female rate) for participation, unemployment, and youth unemployment

Default: true

include_sector_employmentboolean

Include employment by sector breakdown (agriculture, industry, services) even when a different demographic filter is selected

Default: true

time_periodstring

Time period: 'latest', 'last_5_years', 'last_10_years', 'YYYY:YYYY' range, or single 'YYYY' year

Default: latest

include_regional_comparisonboolean

Include comparison with World average, High Income, East Asia & Pacific, and Latin America & Caribbean

Default: true

include_trendsboolean

Include trend analysis with CAGR calculations when historical data is available

Default: true

curl -X POST "https://api.agentpmt.com/products/purchase" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ********" \
  -d '{
    "product_id": "6980dea071cad8f61bf5b1eb",
    "parameters": {
      "action": "query_labor_data",
      "country_or_region": "example_country_or_region",
      "calculate_gender_gaps": true,
      "include_sector_employment": true,
      "time_period": "latest",
      "include_regional_comparison": true,
      "include_trends": true
    }
  }'

import requests
import json

url = "https://api.agentpmt.com/products/purchase"

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer ********"
}

data = {
    "product_id": "6980dea071cad8f61bf5b1eb",
    "parameters": {
        "action": "query_labor_data",
        "country_or_region": "example_country_or_region",
        "calculate_gender_gaps": true,
        "include_sector_employment": true,
        "time_period": "latest",
        "include_regional_comparison": true,
        "include_trends": true
    }
}

response = requests.post(url, headers=headers, json=data)
print(response.status_code)
print(response.json())

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "6980dea071cad8f61bf5b1eb",
  parameters: {
    "action": "query_labor_data",
    "country_or_region": "example_country_or_region",
    "calculate_gender_gaps": true,
    "include_sector_employment": true,
    "time_period": "latest",
    "include_regional_comparison": true,
    "include_trends": true
  }
};

fetch(url, {
  method: "POST",
  headers,
  body: JSON.stringify(data)
})
  .then(response => response.json())
  .then(data => console.log(data))
  .catch(error => console.error("Error:", error));

const axios = require('axios');

const url = "https://api.agentpmt.com/products/purchase";

const headers = {
  "Content-Type": "application/json",
  "Authorization": "Bearer ********"
};

const data = {
  product_id: "6980dea071cad8f61bf5b1eb",
  parameters: {
    "action": "query_labor_data",
    "country_or_region": "example_country_or_region",
    "calculate_gender_gaps": true,
    "include_sector_employment": true,
    "time_period": "latest",
    "include_regional_comparison": true,
    "include_trends": true
  }
};

axios.post(url, data, { headers })
  .then(response => {
    console.log(response.status);
    console.log(response.data);
  })
  .catch(error => {
    console.error("Error:", error.message);
  });

Login to view your API and budget keys. The example above uses placeholder values. Sign in to see personalized code with your bearer token.

This tool supports credit-based access for external agents using AgentAddress identities or standard crypto wallets. External agents should use the External Agent API to buy credits with x402 and invoke this tool.

1. Buy Credits

Purchase credits via x402 payment (500 credit minimum, 100 credits = $1).

# Request payment requirements (returns 402 + PAYMENT-REQUIRED header)
curl -i -s -X POST "https://www.agentpmt.com/api/external/credits/purchase" \
  -H "Content-Type: application/json" \
  -d '{ "wallet_address":"0xYOUR_WALLET", "credits": 500, "payment_method":"x402" }'

# Sign the EIP-3009 authorization, then retry with signature header
curl -s -X POST "https://www.agentpmt.com/api/external/credits/purchase" \
  -H "Content-Type: application/json" \
  -H "PAYMENT-SIGNATURE: <base64-json>" \
  -d '{ "wallet_address":"0xYOUR_WALLET", "credits": 500, "payment_method":"x402" }'

2. Create a Session Nonce (nonce used in signed balance/invoke)

curl -s -X POST "https://www.agentpmt.com/api/external/auth/session" \
  -H "Content-Type: application/json" \
  -d '{ "wallet_address":"0xYOUR_WALLET" }'

3. Invoke This Tool

Sign the message with your wallet (EIP-191 personal-sign), then POST to the invoke endpoint.

# Sign this message (wallet MUST be lowercased):
# agentpmt-external
# wallet:0xyourwallet...
# session:<session_nonce>
# request:<request_id>
# method:POST
# path:/external/tools/labor-market-employment/actions/<actionSlug>/invoke
# payload:<sha256(canonical_json(parameters))>

curl -s -X POST "https://www.agentpmt.com/api/external/tools/labor-market-employment/actions/<actionSlug>/invoke" \
  -H "Content-Type: application/json" \
  -d '{
    "wallet_address": "0xYOUR_WALLET",
    "session_nonce": "<session_nonce>",
    "request_id": "invoke-uuid",
    "signature": "0x<signature>",
    "parameters": {
      "your_param": "value"
    }
  }'

Usage Instructions

Usage guidance provided directly by the developer for this product.

Labor Market & Employment Data

Access comprehensive labor market and employment statistics from the World Bank's World Development Indicators database covering 200+ countries. Query labor force participation, unemployment rates, employment by sector, gender gap analysis, employment quality metrics, and regional comparisons using natural language country names.

Actions

query_labor_data

Fetch labor market and employment indicator data for a country or region.

Required Parameters:

action (string): Must be "query_labor_data"
country_or_region (string): Country or region name in plain language (e.g., "Kenya", "South Korea", "World")

Optional Parameters:

demographic_filter (string, default: null): Filter indicators by demographic category. One of:
- "gender" - Male/female labor force participation and unemployment rates, plus total rates
- "youth" - Youth unemployment (ages 15-24) by gender, plus total unemployment and labor force participation
- "sector" - Employment by economic sector (agriculture, industry, services), plus total labor force and unemployment
- "total" - Overall labor force participation, unemployment, wage/salaried workers, and vulnerable employment
- null or "all" - All available labor market indicators
calculate_gender_gaps (boolean, default: true): Calculate gender gaps as male rate minus female rate for labor force participation, unemployment, and youth unemployment. Positive gap means males higher, negative means females higher.
include_sector_employment (boolean, default: true): Include employment by sector breakdown (agriculture, industry, services) even when a different demographic filter is selected
time_period (string, default: "latest"): Time period for data. Options:
- "latest" - Most recent available data
- "last_5_years" - Last 5 years of data
- "last_10_years" - Last 10 years of data
- "YYYY:YYYY" - Specific year range (e.g., "2015:2020"). Years must be between 1960 and current year.
- "YYYY" - A single year (e.g., "2020")
include_regional_comparison (boolean, default: true): Include comparison data with World average, High Income countries, East Asia & Pacific, and Latin America & Caribbean
include_trends (boolean, default: true): Include trend analysis with CAGR (Compound Annual Growth Rate) calculations when historical data is available

Example - Gender-disaggregated data with gap analysis:

{
  "action": "query_labor_data",
  "country_or_region": "Kenya",
  "demographic_filter": "gender",
  "calculate_gender_gaps": true,
  "time_period": "latest"
}

Example - Youth unemployment trends:

{
  "action": "query_labor_data",
  "country_or_region": "Spain",
  "demographic_filter": "youth",
  "time_period": "last_10_years",
  "include_trends": true
}

Example - All labor indicators with full analysis:

{
  "action": "query_labor_data",
  "country_or_region": "India",
  "demographic_filter": "all",
  "calculate_gender_gaps": true,
  "include_sector_employment": true,
  "time_period": "latest",
  "include_regional_comparison": true
}

Example - Sector employment breakdown:

{
  "action": "query_labor_data",
  "country_or_region": "Vietnam",
  "demographic_filter": "sector",
  "include_sector_employment": true,
  "time_period": "latest"
}

Example - Regional labor market overview:

{
  "action": "query_labor_data",
  "country_or_region": "Sub-Saharan Africa",
  "demographic_filter": "total",
  "time_period": "last_5_years",
  "include_regional_comparison": true
}

Supported Countries and Regions

Accepts plain-language names. Examples of supported inputs:

Countries: "United States", "USA", "India", "South Korea", "Nigeria", "Brazil", "Spain", "Kenya", etc. (200+ countries)
Regions: "Sub-Saharan Africa", "Latin America", "Middle East", "South Asia", "East Asia", "Europe"
Income groups: "Low Income", "Lower Middle Income", "Upper Middle Income", "High Income", "OECD"
Global: "World" or "Global"
ISO3 codes: 3-letter codes like "USA", "IND", "KOR" are also accepted

Country names support partial matching, so "UK" maps to United Kingdom and "America" maps to USA.

Available Indicators

Labor Force Participation

Labor Force Participation Rate, Total (% of population ages 15+)
Labor Force Participation Rate, Male (% of male population ages 15+)
Labor Force Participation Rate, Female (% of female population ages 15+)

Unemployment

Unemployment Rate, Total (% of total labor force)
Unemployment Rate, Male (% of male labor force)
Unemployment Rate, Female (% of female labor force)
Youth Unemployment Rate (% of total labor force ages 15-24)
Youth Unemployment Rate, Male (% ages 15-24)
Youth Unemployment Rate, Female (% ages 15-24)

Employment by Sector

Employment in Agriculture (% of total employment)
Employment in Industry (% of total employment)
Employment in Services (% of total employment)

Employment Quality

Wage and Salaried Workers (% of total employment) -- proxy for formal employment
Vulnerable Employment (% of total employment) -- own-account workers + contributing family workers
Self-Employed (% of total employment)

Employment by Gender

Employment to Population Ratio, Male (%)
Employment to Population Ratio, Female (%)

Other

Part-Time Employment (% of total employment)

Response Structure

Responses include:

Indicator data with latest values, units (% of labor force, % of population ages 15+, %), year, country name, and source
Trend analysis with absolute change, percent change, direction (increasing/decreasing/stable), data point count, and CAGR when applicable
Gender gap analysis (when enabled): labor force participation gap, unemployment gap, and youth unemployment gap with interpretation of gap direction and magnitude
Regional comparison benchmarked against World average, High Income countries, East Asia & Pacific, and Latin America & Caribbean
Insights covering participation levels, unemployment severity, youth unemployment, gender gaps, sector composition (agriculture-dependent vs service-based vs industrial), vulnerable employment, formal employment levels, and SDG 8 progress
SDG 8.5 alignment (Full and productive employment and decent work for all)

Gender Gap Analysis

When calculate_gender_gaps is true, the response includes:

Labor force participation gap: Male rate minus female rate with interpretation
Unemployment gap: Male rate minus female rate with interpretation
Youth unemployment gap: Male youth rate minus female youth rate

Positive gap = males higher. Negative gap = females higher.

Workflows

Gender Equity Assessment: Use demographic_filter: "gender" with calculate_gender_gaps: true to analyze male-female disparities in labor force participation and unemployment
Youth Employment Crisis: Use demographic_filter: "youth" with time_period: "last_10_years" to track youth unemployment trends
Economic Structure Analysis: Use demographic_filter: "sector" to understand whether a country is agriculture-dependent, service-based, or industrial
Job Quality Assessment: Query all indicators and review vulnerable employment and wage/salaried worker percentages as proxies for informal vs formal employment
SDG 8 Monitoring: Query total labor indicators with regional comparison to assess progress toward decent work goals

Notes

Data sourced from World Bank World Development Indicators
Labor force participation = % of population ages 15+ that is economically active
Unemployment = % of labor force without work but available and seeking employment
Youth unemployment covers ages 15-24
Vulnerable employment = own-account workers + contributing family workers (proxy for job insecurity)
Wage/salaried workers serves as a proxy for formal employment
Employment by sector percentages should sum to approximately 100% (agriculture + industry + services)
Gender gaps are calculated as male rate minus female rate
When demographic_filter is unrecognized, the tool defaults to total labor force indicators
When include_sector_employment is true and the demographic filter is not already "sector", sector indicators are appended to the results
Regional comparison uses lfp_total, lfp_female, unemployment_total, and unemployment_youth as key benchmarking indicators
Data availability varies by country; some developing countries may have limited labor force survey coverage
Most recent data is typically 1-2 years behind the current year
Trend CAGR is only calculated for multi-year periods where both start and end values are positive

Global Labor & Employment Data

Available ActionsEach successful request consumes credits as outlined below.

Details

Use Cases

Connect Your Agent In 5 Min

Or Install Locally

Looking for help integrating AI into your business? Set up a free consultation.